18 results on '"Seegerer P"'
Search Results
2. DNA methylation-based classification of sinonasal tumors
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Philipp Jurmeister, Stefanie Glöß, Renée Roller, Maximilian Leitheiser, Simone Schmid, Liliana H. Mochmann, Emma Payá Capilla, Rebecca Fritz, Carsten Dittmayer, Corinna Friedrich, Anne Thieme, Philipp Keyl, Armin Jarosch, Simon Schallenberg, Hendrik Bläker, Inga Hoffmann, Claudia Vollbrecht, Annika Lehmann, Michael Hummel, Daniel Heim, Mohamed Haji, Patrick Harter, Benjamin Englert, Stephan Frank, Jürgen Hench, Werner Paulus, Martin Hasselblatt, Wolfgang Hartmann, Hildegard Dohmen, Ursula Keber, Paul Jank, Carsten Denkert, Christine Stadelmann, Felix Bremmer, Annika Richter, Annika Wefers, Julika Ribbat-Idel, Sven Perner, Christian Idel, Lorenzo Chiariotti, Rosa Della Monica, Alfredo Marinelli, Ulrich Schüller, Michael Bockmayr, Jacklyn Liu, Valerie J. Lund, Martin Forster, Matt Lechner, Sara L. Lorenzo-Guerra, Mario Hermsen, Pascal D. Johann, Abbas Agaimy, Philipp Seegerer, Arend Koch, Frank Heppner, Stefan M. Pfister, David T. W. Jones, Martin Sill, Andreas von Deimling, Matija Snuderl, Klaus-Robert Müller, Erna Forgó, Brooke E. Howitt, Philipp Mertins, Frederick Klauschen, and David Capper
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Science - Abstract
Sinonasal tumour diagnosis can be complicated by the heterogeneity of disease and classification systems. Here, the authors use machine learning to classify sinonasal undifferentiated carcinomas into 4 molecular classe with differences in differentiation state and clinical outcome.
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- 2022
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3. New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning
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Patrick Wagner, Nils Strodthoff, Patrick Wurzel, Arturo Marban, Sonja Scharf, Hendrik Schäfer, Philipp Seegerer, Andreas Loth, Sylvia Hartmann, Frederick Klauschen, Klaus-Robert Müller, Wojciech Samek, and Martin-Leo Hansmann
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Medicine ,Science - Abstract
Abstract Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning. Based on tracks for T-cells (CD3), B-cells (CD20), follicular T-helper cells (PD1) and optical flow of follicular dendritic cells (CD35), we put forward the first quantitative analysis of movement-related and morphological parameters within human lymphoid tissue. We identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment. In addition, we investigated the value of movement and/or morphological parameters for a precise definition of cell types (CD clusters). CD-clusters could be determined based on movement and/or morphology. Differentiating between CD3- and CD20 positive cells is most challenging and long term-movement characteristics are indispensable. We propose morphological and movement-related prototypes of cell entities applying machine learning models. Finally, we define beyond CD clusters new subgroups within lymphocyte entities based on long term movement characteristics. In conclusion, we showed that the combination of 4D imaging and machine learning is able to define characteristics of lymphocytes not visible in 2D histology.
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- 2022
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4. Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia
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Malo Gaubert, Andrea Dell’Orco, Catharina Lange, Antoine Garnier-Crussard, Isabella Zimmermann, Martin Dyrba, Marco Duering, Gabriel Ziegler, Oliver Peters, Lukas Preis, Josef Priller, Eike Jakob Spruth, Anja Schneider, Klaus Fliessbach, Jens Wiltfang, Björn H. Schott, Franziska Maier, Wenzel Glanz, Katharina Buerger, Daniel Janowitz, Robert Perneczky, Boris-Stephan Rauchmann, Stefan Teipel, Ingo Kilimann, Christoph Laske, Matthias H. Munk, Annika Spottke, Nina Roy, Laura Dobisch, Michael Ewers, Peter Dechent, John Dylan Haynes, Klaus Scheffler, Emrah Düzel, Frank Jessen, Miranka Wirth, for the DELCODE study group, Amthauer Holger, Cetindag Arda Can, Cosma Nicoleta Carmen, Diesing Dominik, Ehrlich Marie, Fenski Frederike, Freiesleben Silka Dawn, Fuentes Manuel, Hauser Dietmar, Hujer Nicole, Incesoy Enise Irem, Kainz Christian, Lange Catharina, Lindner Katja, Megges Herlind, Peters Oliver, Preis Lukas, Altenstein Slawek, Lohse Andrea, Franke Christiana, Priller Josef, Spruth Eike, Villar Munoz Irene, Barkhoff Miriam, Boecker Henning, Brosseron Frederic, Daamen Marcel, Engels Tanja, Faber Jennifer, Fließbach Klaus, Frommann Ingo, Grobe-Einsler Marcus, Hennes Guido, Herrmann Gabi, Jost Lorraine, Kalbhen Pascal, Kimmich Okka, Kobeleva Xenia, Kofler Barbara, McCormick Cornelia, Miebach Lisa, Miklitz Carolin, Müller Anna, Oender Demet, Polcher Alexandra, Purrer Veronika, Röske Sandra, Schneider Christine, Schneider Anja, Spottke Annika, Vogt Ina, Wagner Michael, wolfsgruber Steffen, Yilmaz Sagik, Bartels Claudia, Dechent Peter, Hansen Niels, Hassoun Lina, Hirschel Sina, Nuhn Sabine, Pfahlert Ilona, Rausch Lena, Schott Björn, Timäus Charles, Werner Christine, Wiltfang Jens, Zabel Lioba, Zech Heike, Bader Abdelmajid, Baldermann Juan Carlos, Dölle Britta, Drzezga Alexander, Escher Claus, Ghiasi Nasim Roshan, Hardenacke Katja, Jessen Frank, Lützerath Hannah, Maier Franziska, Marquardt Benjamin, Martikke Anja, Meiberth Dix, Petzler Snjezana, Rostamzadeh Ayda, Sannemann Lena, Schild Ann-Katrin, Sorgalla Susanne, Stockter Simone, Thelen Manuela, Tscheuschler Maike, Uhle Franziska, Zeyen Philip, Bittner Daniel, Cardenas-Blanco Arturo, Dobisch Laura, Düzel Emrah, Grieger-Klose Doreen, Hartmann Deike, Metzger Coraline, Nestor Peter, Ruß Christin, Schulze Franziska, Speck Oliver, Yakupov Renat, Ziegler Gabriel, Brauneis Christine, Bürger Katharina, Catak Cihan, Coloma Andrews Lisa, Dichgans Martin, Dörr Angelika, Ertl-Wagner Birgit, Frimmer Daniela, Huber Brigitte, Janowitz Daniel, Kreuzer Max, Markov Eva, Müller Claudia, Rominger Axel, Schmid (ehemals Spreider) Jennifer, Seegerer Anna, Stephan Julia, Zollver Adelgunde, Burow Lena, de Jonge Sylvia, Falkai Peter, Garcia Angarita Natalie, Görlitz Thomas, Gürsel Selim Üstün, Horvath Ildiko, Kurz Carolin, Meisenzahl-Lechner Eva, Perneczky Robert, Utecht Julia, Dyrba Martin, Janecek-Meyer Heike, Kilimann Ingo, Lappe Chris, Lau Esther, Pfaff Henrike, Raum Heike, Sabik Petr, Schmidt Monika, Schulz Heike, Schwarzenboeck Sarah, Teipel Stefan, Weber Marc-Andre, Buchmann Martina, Heger Tanja, Hinderer Petra, Kuder-Buletta Elke, Laske Christoph, Munk Matthias, Mychajliw Christian, Soekadar Surjo, sulzer Patricia, and Trunk Theresia
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white matter hyperintensities segmentation ,evaluation ,FLAIR ,deep learning ,aging ,Alzheimer’s disease ,Psychiatry ,RC435-571 - Abstract
BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.
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- 2023
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5. DNA methylation-based classification of sinonasal tumors
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Jurmeister, Philipp, Glöß, Stefanie, Roller, Renée, Leitheiser, Maximilian, Schmid, Simone, Mochmann, Liliana H., Payá Capilla, Emma, Fritz, Rebecca, Dittmayer, Carsten, Friedrich, Corinna, Thieme, Anne, Keyl, Philipp, Jarosch, Armin, Schallenberg, Simon, Bläker, Hendrik, Hoffmann, Inga, Vollbrecht, Claudia, Lehmann, Annika, Hummel, Michael, Heim, Daniel, Haji, Mohamed, Harter, Patrick, Englert, Benjamin, Frank, Stephan, Hench, Jürgen, Paulus, Werner, Hasselblatt, Martin, Hartmann, Wolfgang, Dohmen, Hildegard, Keber, Ursula, Jank, Paul, Denkert, Carsten, Stadelmann, Christine, Bremmer, Felix, Richter, Annika, Wefers, Annika, Ribbat-Idel, Julika, Perner, Sven, Idel, Christian, Chiariotti, Lorenzo, Della Monica, Rosa, Marinelli, Alfredo, Schüller, Ulrich, Bockmayr, Michael, Liu, Jacklyn, Lund, Valerie J., Forster, Martin, Lechner, Matt, Lorenzo-Guerra, Sara L., Hermsen, Mario, Johann, Pascal D., Agaimy, Abbas, Seegerer, Philipp, Koch, Arend, Heppner, Frank, Pfister, Stefan M., Jones, David T. W., Sill, Martin, von Deimling, Andreas, Snuderl, Matija, Müller, Klaus-Robert, Forgó, Erna, Howitt, Brooke E., Mertins, Philipp, Klauschen, Frederick, and Capper, David
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- 2022
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6. New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning
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Wagner, Patrick, Strodthoff, Nils, Wurzel, Patrick, Marban, Arturo, Scharf, Sonja, Schäfer, Hendrik, Seegerer, Philipp, Loth, Andreas, Hartmann, Sylvia, Klauschen, Frederick, Müller, Klaus-Robert, Samek, Wojciech, and Hansmann, Martin-Leo
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- 2022
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7. Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
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Matthias Zuerl, Philip Stoll, Ingrid Brehm, René Raab, Dario Zanca, Samira Kabri, Johanna Happold, Heiko Nille, Katharina Prechtel, Sophie Wuensch, Marie Krause, Stefan Seegerer, Lorenzo von Fersen, and Bjoern Eskofier
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animal welfare ,animal behavior ,deep learning ,object detection ,animal monitoring ,behavior observation ,Veterinary medicine ,SF600-1100 ,Zoology ,QL1-991 - Abstract
The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.
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- 2022
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8. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
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Hägele, Miriam, Seegerer, Philipp, Lapuschkin, Sebastian, Bockmayr, Michael, Samek, Wojciech, Klauschen, Frederick, Müller, Klaus-Robert, and Binder, Alexander
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- 2020
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9. Decreased CSF Levels of ß-Amyloid in Patients With Cortical Superficial Siderosis
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Cihan Catak, Marialuisa Zedde, Rainer Malik, Daniel Janowitz, Vivian Soric, Anna Seegerer, Alexander Krebs, Marco Düring, Christian Opherk, Jennifer Linn, and Frank A. Wollenweber
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cerebral amyloid angiopathy ,cortical superficial siderosis ,cerebrospinal fluid ,cerebral microbleeds ,neuroimaging ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Background: Cortical superficial siderosis (cSS) represents a key neuroimaging marker of cerebral amyloid angiopathy (CAA) that is associated with intracranial hemorrhages and cognitive impairment. Nevertheless, the association between cSS and core cerebrospinal fluid (CSF) biomarkers for dementia remain unclear.Methods: One hundred and one patients with probable (79%, 80/101) or possible (21%, 21/101) CAA according to the modified Boston criteria and mild cognitive impairment according to Petersen criteria were prospectively included between 2011 and 2016. CSF analyses of ß-amyloid 42, ß-amyloid 40, total tau and phosphorylated tau were performed using sandwich-type enzyme-linked immunosorbent-assay. All patients received MRI and Mini-Mental-State Examination (MMSE). Logistic regression analysis was used to adjust for possible confounders.Results: cSS was present in 61% (62/101). Of those, 53% (33/62) had disseminated cSS and 47% (29/62) focal cSS. ß-amyloid 42 was lower in patients with cSS than in patients without cSS (OR 0.2; 95% CI 0.08–0.6; p = 0.0052) and lower in patients with disseminated cSS than in those with focal cSS (OR 0.02; 95% CI 0.003–0.2; p = 0.00057). Presence of cSS had no association with regard to ß-amyloid 40, total tau and phosphorylated tau.Conclusions: Our results demonstrate that the presence and extent of cSS are associated with reduced CSF ß-amyloid 42 levels. Further studies are needed to investigate the underlying mechanisms of this association.
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- 2019
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10. Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart.
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Elham Kayvanpour, Tommaso Mansi, Farbod Sedaghat-Hamedani, Ali Amr, Dominik Neumann, Bogdan Georgescu, Philipp Seegerer, Ali Kamen, Jan Haas, Karen S Frese, Maria Irawati, Emil Wirsz, Vanessa King, Sebastian Buss, Derliz Mereles, Edgar Zitron, Andreas Keller, Hugo A Katus, Dorin Comaniciu, and Benjamin Meder
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Medicine ,Science - Abstract
BackgroundDespite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders.Methods and resultsState-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters.ConclusionThis paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.
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- 2015
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11. Chemical Exchange Saturation Transfer in Chemical Reactions: A Mechanistic Tool for NMR Detection and Characterization of Transient Intermediates.
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Lokesh, N., Seegerer, Andreas, Hioe, Johnny, and Gschwind, Ruth M.
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- 2018
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12. Remote-Stereocontrol In Dienamine Catalysis: Z-Dienamine Preferences and Electrophile-Catalyst Interaction Revealed by NMR and Computational Studies.
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Seegerer, Andreas, Hioe, Johnny, Hammer, Michael M., Morana, Fabio, Fuchs, Patrick J. W., and Gschwind, Ruth M.
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- 2016
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13. Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning.
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Klauschen, F., Müller, K.-R., Binder, A., Bockmayr, M., Hägele, M., Seegerer, P., Wienert, S., Pruneri, G., de Maria, S., Badve, S., Michiels, S., Nielsen, T.O., Adams, S., Savas, P., Symmans, F., Willis, S., Gruosso, T., Park, M., Haibe-Kains, B., and Gallas, B.
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MACHINE learning , *LYMPHOCYTES , *LEUCOCYTES , *STANDARDIZATION , *INDUSTRIAL engineering , *IMAGE analysis - Abstract
Abstract The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their "black-box" characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics. [ABSTRACT FROM AUTHOR]
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- 2018
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14. Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights.
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Schweizer L, Seegerer P, Kim HY, Saitenmacher R, Muench A, Barnick L, Osterloh A, Dittmayer C, Jödicke R, Pehl D, Reinhardt A, Ruprecht K, Stenzel W, Wefers AK, Harter PN, Schüller U, Heppner FL, Alber M, Müller KR, and Klauschen F
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- Humans, Artificial Intelligence, Neural Networks, Computer, Image Processing, Computer-Assisted methods, Deep Learning
- Abstract
Aim: Analysis of cerebrospinal fluid (CSF) is essential for diagnostic workup of patients with neurological diseases and includes differential cell typing. The current gold standard is based on microscopic examination by specialised technicians and neuropathologists, which is time-consuming, labour-intensive and subjective., Methods: We, therefore, developed an image analysis approach based on expert annotations of 123,181 digitised CSF objects from 78 patients corresponding to 15 clinically relevant categories and trained a multiclass convolutional neural network (CNN)., Results: The CNN classified the 15 categories with high accuracy (mean AUC 97.3%). By using explainable artificial intelligence (XAI), we demonstrate that the CNN identified meaningful cellular substructures in CSF cells recapitulating human pattern recognition. Based on the evaluation of 511 cells selected from 12 different CSF samples, we validated the CNN by comparing it with seven board-certified neuropathologists blinded for clinical information. Inter-rater agreement between the CNN and the ground truth was non-inferior (Krippendorff's alpha 0.79) compared with the agreement of seven human raters and the ground truth (mean Krippendorff's alpha 0.72, range 0.56-0.81). The CNN assigned the correct diagnostic label (inflammatory, haemorrhagic or neoplastic) in 10 out of 11 clinical samples, compared with 7-11 out of 11 by human raters., Conclusions: Our approach provides the basis to overcome current limitations in automated cell classification for routine diagnostics and demonstrates how a visual explanation framework can connect machine decision-making with cell properties and thus provide a novel versatile and quantitative method for investigating CSF manifestations of various neurological diseases., (© 2022 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society.)
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- 2023
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15. Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation.
- Author
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Leitheiser M, Capper D, Seegerer P, Lehmann A, Schüller U, Müller KR, Klauschen F, Jurmeister P, and Bockmayr M
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- Humans, Machine Learning, Neural Networks, Computer, Squamous Cell Carcinoma of Head and Neck genetics, DNA Methylation, Head and Neck Neoplasms genetics
- Abstract
In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic work-up for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models [random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), and support vector machine (SVM)] that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF = 83%, NN = 88%, LOGREG = SVM = 89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites, and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic work-up of HNSC-CUP. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)
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- 2022
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16. Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases.
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Jurmeister P, Bockmayr M, Seegerer P, Bockmayr T, Treue D, Montavon G, Vollbrecht C, Arnold A, Teichmann D, Bressem K, Schüller U, von Laffert M, Müller KR, Capper D, and Klauschen F
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- Algorithms, Cohort Studies, Humans, Reproducibility of Results, Carcinoma, Squamous Cell genetics, Carcinoma, Squamous Cell secondary, DNA Methylation genetics, Head and Neck Neoplasms genetics, Head and Neck Neoplasms pathology, Lung Neoplasms genetics, Lung Neoplasms secondary, Machine Learning
- Abstract
Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)
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- 2019
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17. Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart.
- Author
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Kayvanpour E, Mansi T, Sedaghat-Hamedani F, Amr A, Neumann D, Georgescu B, Seegerer P, Kamen A, Haas J, Frese KS, Irawati M, Wirsz E, King V, Buss S, Mereles D, Zitron E, Keller A, Katus HA, Comaniciu D, and Meder B
- Subjects
- Heart Failure pathology, Heart Failure physiopathology, Humans, Heart Failure therapy, Precision Medicine
- Abstract
Background: Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders., Methods and Results: State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters., Conclusion: This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.
- Published
- 2015
- Full Text
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18. Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals.
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Zettinig O, Mansi T, Neumann D, Georgescu B, Rapaka S, Seegerer P, Kayvanpour E, Sedaghat-Hamedani F, Amr A, Haas J, Steen H, Katus H, Meder B, Navab N, Kamen A, and Comaniciu D
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- Computer Simulation, Humans, Reproducibility of Results, Sensitivity and Specificity, Body Surface Potential Mapping methods, Cardiomyopathy, Dilated diagnosis, Cardiomyopathy, Dilated physiopathology, Diagnosis, Computer-Assisted methods, Electrocardiography methods, Heart Conduction System physiopathology, Models, Cardiovascular
- Abstract
Diagnosis and treatment of dilated cardiomyopathy (DCM) is challenging due to a large variety of causes and disease stages. Computational models of cardiac electrophysiology (EP) can be used to improve the assessment and prognosis of DCM, plan therapies and predict their outcome, but require personalization. In this work, we present a data-driven approach to estimate the electrical diffusivity parameter of an EP model from standard 12-lead electrocardiograms (ECG). An efficient forward model based on a mono-domain, phenomenological Lattice-Boltzmann model of cardiac EP, and a boundary element-based mapping of potentials to the body surface is employed. The electrical diffusivity of myocardium, left ventricle and right ventricle endocardium is then estimated using polynomial regression which takes as input the QRS duration and electrical axis. After validating the forward model, we computed 9500 EP simulations on 19 different DCM patients in just under three seconds each to learn the regression model. Using this database, we quantify the intrinsic uncertainty of electrical diffusion for given ECG features and show in a leave-one-patient-out cross-validation that the regression method is able to predict myocardium diffusion within the uncertainty range. Finally, our approach is tested on the 19 cases using their clinical ECG. 84% of them could be personalized using our method, yielding mean prediction errors of 18.7ms for the QRS duration and 6.5° for the electrical axis, both values being within clinical acceptability. By providing an estimate of diffusion parameters from readily available clinical data, our data-driven approach could therefore constitute a first calibration step toward a more complete personalization of cardiac EP., (Copyright © 2014 Elsevier B.V. All rights reserved.)
- Published
- 2014
- Full Text
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